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import gradio as gr
import time
import torch
from transformers import BertTokenizer, BertForSequenceClassification
label_dict = {"Urgency": 0, "Not Dark Pattern": 1, "Scarcity": 2, "Misdirection": 3, "Social Proof": 4, "Obstruction": 5, "Sneaking": 6, "Forced Action": 7}
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=len(label_dict))
fine_tuned_model_path = "models/finetuned_BERT_5k_epoch_5.model"
model.load_state_dict(torch.load(fine_tuned_model_path, map_location=torch.device('cpu')))
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
def get_dark_pattern_name(label):
reverse_label_dict = {v: k for k, v in label_dict.items()}
return reverse_label_dict[label]
def find_dark_pattern(text_predict):
encoded_text = tokenizer.encode_plus(
text_predict,
add_special_tokens=True,
return_attention_mask=True,
pad_to_max_length=True,
max_length=256,
return_tensors='pt'
)
model.eval()
with torch.no_grad():
inputs = {
'input_ids': encoded_text['input_ids'],
'attention_mask': encoded_text['attention_mask']
}
outputs = model(**inputs)
predictions = outputs.logits
probabilities = torch.nn.functional.softmax(predictions, dim=1)
predicted_label = torch.argmax(probabilities, dim=1).item()
return get_dark_pattern_name(predicted_label)
def predict(text_to_predict):
start_time = time.time()
print("Predicting Dark Pattern...")
for i in range(10):
predicted_darkp = find_dark_pattern(text_to_predict)
time.sleep(0.5)
end_time = time.time()
total_time = end_time - start_time
return predicted_darkp
demo = gr.Interface(fn=predict, inputs="text", outputs="text")
demo.launch(share=True)
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